Utilizing virtualization technology to combine real-time operating system (RTOS) and off-the-shelf time-sharing general purpose operating system (GPOS) is attracting much more interest recently. Such combination has t...
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In order to get rid of the limit of traditional methods and provide a decision making reference for the supervision of securities organizations and the risk control of investors, A novel model based on SOM2W network (...
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Porting an application written for personal computer to embedded devices requires conversion of floating-point numbers and operations into fixed-point ones. Testing the conversion hence requires the latter be as close...
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In this paper, we propose a taxonomy that characterizes and classifies different components of autonomic application management in Grids. We also survey several representative Grid systems developed by various project...
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In this paper, we propose a taxonomy that characterizes and classifies different components of autonomic application management in Grids. We also survey several representative Grid systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the similarities and differences of state-of-the-art technologies utilized in autonomic application management from the perspective of Grid computing, but also identifies the areas that require further research initiatives.
The cache replacement mechanism in cooperative caching has a significant bearing in determining the caches performance. Valid data items still get evicted from the cache space when new item is to be cached but there i...
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The cache replacement mechanism in cooperative caching has a significant bearing in determining the caches performance. Valid data items still get evicted from the cache space when new item is to be cached but there is no space available to hold it. The existence of data items in caches indicates some degree of interest on the data item. Salvaging the evicted valid data item could improve the overall caching performance. In this paper, we propose an efficient cooperative caching scheme known as CacheRescue for wireless mesh networks. The CacheRescue scheme caches data in the mesh routers expandable storage space to hold valid but evicted data items. We have used simulation to evaluate the performance of CacheRescue scheme. The simulation results show that our proposed approach improves the caching performance when compared to other existing and previously proposed caching solutions.
Data hiding in computer system is an interesting and important research issue, which brings benefits for secret communication and watermarking. The development of virtual machine brings new potential for data hiding. ...
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Data hiding in computer system is an interesting and important research issue, which brings benefits for secret communication and watermarking. The development of virtual machine brings new potential for data hiding. In this paper we explore the potential for data hiding in virtual machine disk images, and especially hiding schemes that can be used with copy-on-write images. Besides being considered as a way for valid uses such as secret communication and watermarking, these schemes can be a warning against malicious intentions as well. Furthermore, it lays the foundation for a more thorough analysis of the whole virtual machine system for data hiding.
Microarray technology allows for the simultaneous monitoring of thousands of genes expressions per sample. Unfortunately, the classification of these samples into distinct classes is often difficult as the number of g...
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Microarray technology allows for the simultaneous monitoring of thousands of genes expressions per sample. Unfortunately, the classification of these samples into distinct classes is often difficult as the number of genes (features) greatly exceeds the number of samples. Consequently, there is a need to investigate new, robust machine learning techniques capable of accurately classifying microarray data. In this paper, we present a coevolutionary learning classifier system based on feature set partitioning to classify gene expression data. A distributed implementation, which leverages Cloud computing technologies, is used to address the inherent computational costs of our model. The development and execution of this application was done using the Aneka middleware on the public Cloud (Amazon EC2) infrastructure. Experiments conducted using gene expression profiles demonstrates that the proposed implementation outperforms other well-known classifiers in terms of accuracy. Preliminary analysis into the impact of different Cloud setups on the performance of the classifier are also reported.
Rationale and rationale management have been playing an increasingly prominent role in software system development mainly due to the knowledge demand during system evaluation, maintenance, and evolution, especially fo...
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Rationale and rationale management have been playing an increasingly prominent role in software system development mainly due to the knowledge demand during system evaluation, maintenance, and evolution, especially for large and complex systems. The rationale management for requirements engineering, as a commencing and critical phase in software development life cycle, is still under-exploited. In this paper, we first survey briefly the state-of-the-art on rationale employment and applications in requirements engineering. Secondly, we identify the challenges in integrating rationale management in requirements engineering activities in order to promote further investigations and define a research agenda on rationale management in requirements engineering.
Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data re...
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ISBN:
(纸本)9781424466955;9780769540184
Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the `execution time'. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing `Best Resource Selection' (BRS) algorithm. Our results show that PSO can achieve: (a) as much as 3 times cost savings as compared to BRS, and (b) good distribution of workload onto resources.
Advances in Cloud computing opens up many new possibilities for Internet applications developers. Previously, a main concern of Internet applications developers was deployment and hosting of applications, because it r...
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ISBN:
(纸本)9781424466955;9780769540184
Advances in Cloud computing opens up many new possibilities for Internet applications developers. Previously, a main concern of Internet applications developers was deployment and hosting of applications, because it required acquisition of a server with a fixed capacity able to handle the expected application peak demand and the installation and maintenance of the whole software infrastructure of the platform supporting the application. Furthermore, server was underutilized because peak traffic happens only at specific times. With the advent of the Cloud, deployment and hosting became cheaper and easier with the use of pay-peruse flexible elastic infrastructure services offered by Cloud providers. Because several Cloud providers are available, each one offering different pricing models and located in different geographic regions, a new concern of application developers is selecting providers and data center locations for applications. However, there is a lack of tools that enable developers to evaluate requirements of large-scale Cloud applications in terms of geographic distribution of both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of Cloud environments and applications, we propose CloudAnalyst. It was developed to simulate large-scale Cloud applications with the purpose of studying the behavior of such applications under various deployment configurations. CloudAnalyst helps developers with insights in how to distribute applications among Cloud infrastructures and value added services such as optimization of applications performance and providers incoming with the use of Service Brokers.
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